{"id":"W4316924672","doi":"10.18609/cgti.2022.209","title":"Innovating in iPSC differentiation & engineering","year":2022,"lang":"en","type":"article","venue":"Cell and Gene Therapy Insights","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Chemistry; Cell biology; Engineering; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002623311,0.0004275475,0.0002604543,0.0007951679,0.0003239793,0.001991326,0.000610955,0.0009562998,0.003696455],"category_scores_gemma":[0.001353715,0.0002909989,0.0003170306,0.0006114158,0.001219871,0.001420396,0.0005670037,0.001841051,0.003029236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008147551,"about_ca_system_score_gemma":0.0006760371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003625937,"about_ca_topic_score_gemma":0.0008672282,"domain_scores_codex":[0.9985635,0.0001442688,0.00008300483,0.0001776968,0.0009664254,0.00006518064],"domain_scores_gemma":[0.9988419,0.0003598664,0.00008581141,0.0001205677,0.0004890163,0.000102796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000778986,0.00008490786,0.001056646,0.0005670552,0.00002413159,0.0004789071,0.0004877569,0.002983577,0.1432683,0.1470569,0.05536256,0.6485512],"study_design_scores_gemma":[0.00001374318,0.0001476302,0.0003750007,0.00009670354,0.00001672041,0.001223458,0.0001125396,0.004242535,0.1122934,0.01936368,0.8620882,0.00002638161],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01716633,0.04308347,0.7698108,0.02316206,0.01134063,0.000210786,0.0002606984,0.001306159,0.133659],"genre_scores_gemma":[0.1292761,0.07463467,0.519106,0.01262869,0.003843246,0.0002386505,0.000357537,0.0007945152,0.2591206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003696455,"threshold_uncertainty_score":0.01387352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01158348569898052,"score_gpt":0.1814730039020362,"score_spread":0.1698895182030556,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}